'''simple docstring'''





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]=2_81_23     )       -> str:


     '''simple docstring'''




     UpperCAmelCase_			       =  [1] * (limit + 1)

     for i in range(2   ,       int(limit**0.5     ) + 1     ):
          sum_divs[i * i] += i
          for k in range(i + 1   ,       limit // i + 1     ):
               sum_divs[k * i] += k + i

     UpperCAmelCase_			       =  set()
     UpperCAmelCase_			       =  0

     for n in range(1   ,       limit + 1     ):
          if sum_divs[n] > n:
               abundants.add(snake_case_     )

          if not any((n - a in abundants) for a in abundants     ):
               res += n

     return res


if __name__ == "__main__":
    print(solution())






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






